Discover all organizations/accounts in Source Cooperative.

Returns: List of account IDs (e.g., ['clarkcga', 'harvard-lil', 'youssef-harby'])

Example: >>> await list_accounts() ['addresscloud', 'clarkcga', 'harvard-lil', ...]

List products (datasets) in Source Cooperative with hybrid S3 + API approach.

DEFAULT: Uses S3 direct scan (fast, includes ALL products with file counts). Set include_unpublished=False for published-only with rich metadata from API.

Args: account_id: Filter by specific account. REQUIRED for S3 mode (default). If None with include_unpublished=False, lists published from all accounts. featured_only: Only return featured/curated products (API mode only). include_unpublished: If True (default), scan S3 for ALL products including unpublished. If False, use API for published products with rich metadata. include_file_count: Count files in each product (default True, only in S3 mode).

Returns: S3 mode (default): Basic info (product_id, s3_prefix, file_count) - fast! API mode: Rich metadata (product_id, title, description, dates) - slower

Performance: - S3 mode (default): ~240ms, includes unpublished products + file counts - API mode (include_unpublished=False): ~500ms, rich metadata, published only

Examples: >>> # ALL products with file counts (DEFAULT - fast!) >>> await list_products(account_id="youssef-harby") [ {"product_id": "exiobase-3", "source": "s3", "file_count": 1000, ...}, {"product_id": "egms-copernicus", "source": "s3", "file_count": 53, ...}, ... ]

>>> # Published products with rich metadata (API mode)
>>> await list_products(account_id="youssef-harby", include_unpublished=False)
[{"product_id": "egms-copernicus", "title": "...", "description": "...", ...}]

>>> # Fast mode without file counts
>>> await list_products(account_id="youssef-harby", include_file_count=False)
[{"product_id": "exiobase-3", "source": "s3", ...}]

>>> # Featured products only (requires API mode)
>>> await list_products(featured_only=True, include_unpublished=False)
[{"product_id": "gov-data", "featured": 1, ...}]

Get comprehensive metadata for a specific product. Always includes README content if found in the product root directory.

Args: account_id: Account ID (e.g., "harvard-lil") product_id: Product ID (e.g., "gov-data")

Returns: Full product metadata including account info, storage config, roles, tags Always includes 'readme' field with content and metadata (if README exists)

Example: >>> await get_product_details("harvard-lil", "gov-data") { "title": "Archive of data.gov", "description": "...", "account": {"name": "Harvard Library Innovation Lab", ...}, "readme": { "found": true, "content": "# Archive of data.gov...", "size": 5344, "path": "harvard-lil/gov-data/README.md" }, ... }

List all files in a product with full S3 paths ready for analysis. Optionally show a hierarchical tree visualization (optimized for LLM tokens).

Args: account_id: Account ID product_id: Product ID prefix: Optional prefix to filter files (subdirectory path) max_files: Maximum files to return (default 1000) show_tree: If True, return tree visualization only (more token-efficient, default True)

Returns: Dict with either files list OR tree visualization (not both to save tokens)

Example (List mode - detailed metadata): >>> result = await list_product_files("harvard-lil", "gov-data", "metadata/") >>> print(result["files"][0]) { "key": "harvard-lil/gov-data/metadata/metadata.jsonl.zip", "s3_uri": "s3://us-west-2.opendata.source.coop/harvard-lil/gov-data/metadata/metadata.jsonl.zip", "http_url": "https://data.source.coop/harvard-lil/gov-data/metadata/metadata.jsonl.zip", "size": 1012127330, "last_modified": "2025-02-06T16:20:22+00:00" }

Example (Tree mode - token optimized): >>> result = await list_product_files("harvard-lil", "gov-data", show_tree=True) >>> print(result["tree"]) s3://us-west-2.opendata.source.coop/harvard-lil/gov-data/ ├── README.md (5.2 KB) → s3://...README.md ├── metadata/ │ └── metadata.jsonl.zip (965.4 MB) → s3://...metadata.jsonl.zip └── data/ └── datasets.parquet (128.5 MB) → s3://...datasets.parquet

Example (Partitioned data - smart summarization): >>> result = await list_product_files("account", "product", show_tree=True) >>> print(result["tree"]) s3://us-west-2.opendata.source.coop/account/product/ ├── year={1995,1996,...,2007 (13 total)}/ [partitioned] │ └── format={ixi,pxp}/ [partitioned] │ └── matrix={F_impacts,F_satellite,Y,Z}/ [partitioned] │ └── data.parquet (5.1 MB)

Note: Shows first,second,...,last (total) for >10 values; lists all for ≤10
Tree mode saves ~70% tokens + smart partition detection saves 96%+ more

Get metadata for a specific file without downloading it. Uses obstore's head operation for efficient metadata retrieval.

Args: path: S3 URI (s3://...) or relative path (account_id/product_id/file)

Returns: File metadata: size, content-type, last-modified, etag, URLs

Example: >>> await get_file_metadata("harvard-lil/gov-data/README.md") { "key": "harvard-lil/gov-data/README.md", "content_type": "binary/octet-stream", "content_length": 5344, "last_modified": "2025-02-06T16:29:24+00:00", ... }

Search for products across ALL accounts with smart fuzzy matching. Handles typos, partial matches, and incomplete words using 60% similarity threshold.

Hybrid Search - Automatically searches across:

• All 94+ organizations

• ALL products (published + unpublished)

• All fields: title, description, product_id

Published products: Full metadata (title, description, product_id) Unpublished products: product_id only (no title/description available)

Args: query: Search keyword (supports typos and partial matches)

Returns: Top 5 matching accounts or products (sorted by relevance score)

Performance: ~5-8s (parallel 2-level S3 scan + top 5 API enrichment)

Performance breakdown:
- S3 parallel listing: ~2.4s (94 accounts + 354 products)
- Fuzzy matching: <1s (in-memory processing)
- API enrichment: ~2-5s (only top 5 results)

**11x faster** than sequential approach (was ~27s)
**Uses 2-level delimiter listing** (not full recursive scan)

Examples: >>> # Exact match >>> results = await search("climate")

>>> # Fuzzy match (handles typos)
>>> results = await search("climte")  # Finds "climate"
>>> results = await search("exiopase")  # Finds "exiobase-3" (includes unpublished!)

>>> # Partial match
>>> results = await search("geo")  # Finds "geospatial", "geocoding", etc.

>>> # Result formats
>>> print(results[0])  # Account match
{
    "type": "account",
    "account_id": "harvard-lil",
    "match_string": "harvard-lil",
    "search_score": 9.5,
    "similarity": 0.95,
    "matched_fields": ["account_id"]
}

>>> print(results[1])  # Product match
{
    "type": "product",
    "account_id": "youssef-harby",
    "product_id": "exiobase-3",
    "match_string": "youssef-harby/exiobase-3",
    "title": "",  # Empty for unpublished products
    "description": "",  # Empty for unpublished products
    "search_score": 8.2,
    "similarity": 0.82,
    "matched_fields": ["product_id"]
}